Abstract : Most spatial information systems are limited to a fixed
dimension (generally 2) which is not extensible.
On the other hand, the emerging paradigm of constraint databases
allows the representation of data of arbitrary dimension, together
with abstract query
languages. The complexity of evaluating queries though might be costly
if the dimension of the objects is really arbitrary.
In this paper, we present a data model, based on linear constraints,
dedicated to the representation
and manipulation of multidimensional data.
In order to preserve a low complexity for query evaluation,
we introduce the structural dimension of an object $O$,
as the dimension of the components $O_1,...,O_n$, such that
$O=O_1\times\cdots\times O_n$. This allows to process
queries independently on each component, therefore achieving a
satisfying trade-off between design simplicity, expressive power of
the query language and efficiency of query evaluation.
We illustrate these concepts in the context of spatio-temporal
databases where {\em space} and {\em time} are the natural components.
This data model has been implemented in the \dedale/ system and a
spatio-temporal application, with structural dimension 2, is currently
running, thus showing the practical relevance of the approach.
